Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity

Area of Science:

• Epidemiology
• Health Services Research
• Mathematical Modeling

Background:

• The coronavirus disease 2019 (COVID-19) pandemic placed immense pressure on healthcare systems globally.
• Hospitals faced unprecedented challenges in managing patient surges and resource allocation.

Purpose of the Study:

• To describe methods for forecasting COVID-19 case loads and peak incidence at a university hospital.
• To develop and evaluate predictive models for hospital resource management during a pandemic.

Main Methods:

• Developed a static model using data from prior epidemics and expert opinion.
• Created a dynamic model that incorporated local patient data, effective reproduction number, and real-time admission/discharge rates.
• Compared model performance against observed hospital bed occupancy and peak timing.

Main Results:

• The initial static model overestimated general ward and ICU bed occupancy and predicted peak incidence later than observed.
• The dynamic model, updated daily after April 5, showed improved precision as more local data became available.
• Both models provided advance guidance for resource preparation, with overestimations offering a safety margin.

Conclusions:

• Data-driven forecasting models are crucial for hospital preparedness and resource allocation during public health crises.
• Dynamic models, leveraging real-time local data, offer greater accuracy for predicting patient flow and demand.
• While initial overestimation provides a safety margin, refining models with population contact patterns can enhance predictive accuracy.